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针对医疗应用，实现变光照条件下彩色图像中静脉的可视化。

Visualizing veins from color images under varying illuminations for medical applications.

机构信息

Nanjing University of Aeronautics and Astronautics, College of Automation Engineering, Nanjing, Jian, China.

出版信息

J Biomed Opt. 2021 Sep;26(9). doi: 10.1117/1.JBO.26.9.096006.

DOI:10.1117/1.JBO.26.9.096006
PMID:34541836
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8450381/
Abstract

SIGNIFICANCE

Effective vein visualization is critically important for several clinical procedures, such as venous blood sampling and intravenous injection. Existing technologies using infrared device or ultrasound rely on professional equipment and are not suitable for daily medical care. A regression-based vein visualization method is proposed.

AIM

We visualize veins from conventional RGB images to provide assistance in venipuncture procedures as well as clinical diagnosis of some venous insufficiency.

APPROACH

The RGB images taken by digital cameras are first transformed to spectral reflectance images using Wiener estimation. Multiple regression analysis is then applied to derive the relationship between spectral reflectance and the concentrations of pigments. Monte Carlo simulation is adopted to get prior information. Finally, vein patterns are visualized from the spatial distribution of pigments. To minimize the effect of illumination on skin color, light correction and shading removal operations are performed in advance.

RESULTS

Experimental results from inner forearms of 60 subjects show the effectiveness of the regression-based method. Subjective and objective evaluations demonstrate that the clarity and completeness of vein patterns can be improved by light correction and shading removal.

CONCLUSIONS

Vein patterns can be successfully visualized from RGB images without any professional equipment. The proposed method can assist in venipuncture procedures. It also shows promising potential to be used in clinical diagnosis and treatment of some venous insufficiency.

摘要

意义

有效的静脉可视化对于静脉采血和静脉注射等几种临床操作至关重要。现有的基于红外设备或超声的技术依赖于专业设备，不适合日常医疗保健。本文提出了一种基于回归的静脉可视化方法。

目的

我们从常规的 RGB 图像中可视化静脉，为静脉穿刺程序以及某些静脉功能不全的临床诊断提供帮助。

方法

使用 Wiener 估计将数字相机拍摄的 RGB 图像首先转换为光谱反射率图像。然后应用多元回归分析得出光谱反射率与色素浓度之间的关系。采用蒙特卡罗模拟获得先验信息。最后，从色素的空间分布中可视化静脉模式。为了最小化照明对肤色的影响，在进行分析前会进行光校正和去阴影操作。

结果

来自 60 名受试者前臂内侧的实验结果表明了基于回归的方法的有效性。主观和客观评估表明，光校正和去阴影操作可以提高静脉模式的清晰度和完整性。

结论

无需任何专业设备即可成功从 RGB 图像中可视化静脉模式。该方法可辅助静脉穿刺程序。它在某些静脉功能不全的临床诊断和治疗方面也显示出了有前景的应用潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/a17ad4dbc284/JBO-026-096006-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/e298e9235c58/JBO-026-096006-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/60dad30d0c76/JBO-026-096006-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/4ea54e0df1eb/JBO-026-096006-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/3ce4ab3d6516/JBO-026-096006-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/8fa7ba6105f3/JBO-026-096006-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/2cb413695ed2/JBO-026-096006-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/44d702759068/JBO-026-096006-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/61e65f670eab/JBO-026-096006-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/20849a183302/JBO-026-096006-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/9780547fd5ba/JBO-026-096006-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/a17ad4dbc284/JBO-026-096006-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/e298e9235c58/JBO-026-096006-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/60dad30d0c76/JBO-026-096006-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/4ea54e0df1eb/JBO-026-096006-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/3ce4ab3d6516/JBO-026-096006-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/8fa7ba6105f3/JBO-026-096006-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/2cb413695ed2/JBO-026-096006-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/44d702759068/JBO-026-096006-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/61e65f670eab/JBO-026-096006-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/20849a183302/JBO-026-096006-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/9780547fd5ba/JBO-026-096006-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/894b/8450381/a17ad4dbc284/JBO-026-096006-g011.jpg

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